2025-04-01
” Visualization is a method of computing. It transforms the symbolic into the geometric, enabling researchers to observe their simulations and computations. Visualization offers a method for seeing the unseen. It enriches the process of scientific discovery and fosters profound and unexpected insights. In many fields it is already revolutionizing the way scientists do science.
~ McCormick, B.H., T.A. DeFanti, M.D. Brown, Visualization in Scientific Computing, Computer Graphics Vol. 21.6, November 1987
“Sometimes the most effective way to describe, explore, and summarize a set of numbers - even a very large set - is to look at those numbers”
~ The Visual Display of Quantitative Information, Edwrd Tufte, 1983
[ Find the complete cheatsheet on GitHub in the ‘img/’ folder]
mapping data info into visual info
necessary: data (y-axis elements)
x-axis elements
type of plot: scatter plot, line plot, histogram, bargraph, violin plot, etc.
shape, size, and color specification
axis ticks and labels
legend
title
https://datavizcatalogue.com/index.html https://matplotlib.org/stable/gallery/index.html https://matplotlib.org/cheatsheets/ https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833
convolvedeconvolvesignalwelchfrom scipy import signal
f, Pxx_den = signal.welch(trace, fs=1/time[1], nperseg=1024, average='median')
plt.semilogy(f, Pxx_den)
plt.vlines(125, Pxx_den.min(), Pxx_den.max(),color='r', linestyle='--')
plt.xlabel('Frequency [Hz]')
plt.ylabel('PSD [V**2/Hz]')
plt.title('Power Spectral Density')
plt.show() f and power densities Pxx_denText(0.5, 0, 'Frequency [Hz]')
Text(0, 0.5, 'PSD [V**2/Hz]')
Text(0.5, 1.0, 'Power Spectral Density')
Text(0.5, 1.0, 'Filter frequency response')
Text(0.5, 0, 'Frequency [Hz]')
Text(0, 0.5, 'Gain')